Using natural language processing to automate user feedback analysis

Abstract

The purpose of the study is to develop a system for automated analysis of user feedback based on natural language processing methods to detect tone, key aspects and topics. The object of the study is the processes of processing and analyzing user feedback in a digital environment. The subject of the study is natural language processing methods and models for automating the analysis of text feedback. The research methods include machine learning, deep neural networks, statistical methods of text analysis and methods for assessing the quality of models. Classical algorithms, neural network models and transformer architectures are used. The statistical significance of the results was experimentally confirmed and recommendations for choosing models for various scenarios were developed. The research results can be used in e-commerce, service companies, software development, marketing and analytics to automate the analysis of feedback and identify trends in large arrays of text data.